Introduction

1 2 3 4 5 6

Key Questions

  • How does HIV persist and migrate across CNS and peripheral tissues despite systemic viral suppression?

Overview

This presentation summarizes:

  • Viral phylogenetics & BEAST modeling
  • CNS reservoir compartmentalization
  • Migration dynamics across tissues & BBB



Cohort Workflow

Last Gift Cohort

Body Donation

Autopsy

Tissue Collection

HIV Sequencing

Phylogenetics and Modeling

OBJECTIVES

Blood Brain Interface

Image attribution: BioRender.
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Objectives

Aim 1

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Aim 2

  • Identify CNS-specific clusters (reservoirs)
  • Characterize:
    • Composition
    • Timing
    • Renewal from periphery
    • Reseeding capacity into peripheral reservoirs

APPROACH

Aim 1

Data Processing

  • SGA HIV env clones across tissues
  • CSF biomarkers & clinical metadata
  • BEAST diffusion + phylogenetic modeling
  • Negative Binomial & Poisson models

Workflow

Aim 2

Data Processing

  • HIV env clones across participants & tissues
  • Cleanup & alignment
  • Phylogenetic reconstruction
  • Identify CNS-specific supported clusters
  • Bayesian skyline / skygrid diffusion models
  • Timing (tMRCA) of clusters
  • Migration flows between CNS clusters & other tissues
  • Modeling factors associated with migration

Workflow

Sampling

Note

  • Our analyses included 20 participants with sequences from both the CNS and Periphery.
  • A total of 1875 SGA clones were included from 49 distincts tissues.
  • Luminex CSF data available for 34 markers.

AIM 1: Viral Dynamics

Maximum Clade Credibility Tree

Time-Scaled Maximum Clade Credibility Tree

HIV Migration Networks

HIV Flow across the BBB through time

AIM 1: Reservoirs Dynamic and CSF Markers

Summary

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Approach

  • We compared a patient-level aggregated negative-binomial model (AGG-NB) with a state-level hierarchical negative-binomial model (STATE-NB) that accounted for variation across individual BEAST states.

Data Preparation

  • Aggregated (pid-level counts):We first fit an aggregated-count model (mod_agg_nb), where counts were summed across BEAST states for each participant.”
  • Longitudinal (state-level / transition-level): We then fit a state-level model (mod_long_nb), leveraging the full longitudinal BEAST reconstruction.

Exposure (log)

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Data Input

Exposure

Conceptual motivation

In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Equation

\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]

Model Code

data %>%
  mutate(
    pairs_cns    = ntissues_cns * (ntissues_cns - 1) / 2,
    pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
    
    exposure = case_when(
      migration_type == "cross_BBB" & direction == "CNS to Periph" ~ 
        n_states * nseq_cns * nseq_periph,
      
      migration_type == "cross_BBB" & direction == "Periphery to CNS" ~ 
        n_states * nseq_periph * nseq_cns,
      
      migration_type == "within_CNS" ~ 
        n_states * pairs_cns * (nseq_cns^2),
      
      migration_type == "within_peripheral" ~ 
        n_states * pairs_periph * (nseq_periph^2)
    )
  )

CSF Markers

Data Preparation for Migration Models

Summaries

  • Count migration/transition events from and toward the CNS and **within* CNS
  • Compute number of discrete viral states per run
  • Count SGA clones sampled in CNS and in Periphery

Exposure Definition

  • Aggregated: # states × # CNS sequences × # Periphery sequences
  • long format: # sequences from × # sequences to

Filtering

  • Exclude viremic individuals
  • Exclude individuals without CSF data

Data Transformations

  • Log-transform exposure
  • Log1p-transform CSF marker values and reservoir measures

Workflow

Model Specification: Negative Binomial GLMM

Notes

  • Outcome: count of CNS-related migration/transition events
  • Offset: log(exposure)
  • Predictor: log1p-transformed CSF marker
  • Random effect for participants

Equation

\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]

fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}

fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
      dna_level_from + dna_level_to +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}

Model Results: Aggregated (pid-level counts)

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok ✅ 1.035 0.7967–1.35 0.795 ⬆
GM-CSF 8 Ok ✅ 0.884 0.7446–1.05 0.161 ⬇
GRO-alpha (CXCL1) 13 Ok ✅ 0.988 0.9808–1.00 0.001 ** ⬇
IL-1α 1 Sparse Data ❌ 0.160 0.0022–11.58 0.401 ⬇
IL-1β 5 Ok ✅ 0.806 0.6734–0.96 0.018 * ⬇
IL-1RA 12 Ok ✅ 1.000 0.9995–1.00 0.000 *** ⬇
IL-2 2 Sparse Data ❌ 0.418 0.2951–0.59 0.000 *** ⬇
IL-5 5 Ok ✅ 0.985 0.8082–1.20 0.885 ⬇
IL-6 12 Ok ✅ 0.998 0.9971–1.00 0.000 *** ⬇
IL-7 13 Ok ✅ 0.356 0.0618–2.05 0.248 ⬇
IL-8 (CXCL8) 13 Ok ✅ 0.998 0.9952–1.00 0.043 * ⬇
IL-9 1 Sparse Data ❌ 1.104 0.5813–2.10 0.762 ⬆
IL-10 8 Ok ✅ 0.481 0.2551–0.91 0.023 * ⬇
IL-15 5 Ok ✅ 0.442 0.1617–1.21 0.112 ⬇
IL-17A/CTLA-8 1 Sparse Data ❌ 0.409 0.0534–3.13 0.389 ⬇
IL-18 13 Ok ✅ 1.009 0.9871–1.03 0.435 ⬆
IL-31 1 Sparse Data ❌ 0.932 0.6748–1.29 0.668 ⬇
IP-10 (CXCL10) 13 Ok ✅ 1.000 0.9967–1.00 0.841 ⬇
MCP-1 (CCL2) 13 Ok ✅ 1.000 0.9985–1.00 0.908 ⬇
MIP-1α (CCL3) 13 Ok ✅ 0.868 0.7918–0.95 0.002 ** ⬇
MIP-1β (CCL4) 13 Ok ✅ 0.964 0.9420–0.99 0.002 ** ⬇
RANTES (CCL5) 13 Ok ✅ 1.007 0.9176–1.11 0.881 ⬆
SDF-1α 13 Ok ✅ 1.000 0.9993–1.00 0.798 ⬆
TNF-α 2 Sparse Data ❌ 0.328 0.2148–0.50 0.000 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok ✅ 1.002 0.875–1.15 0.972 ⬆
GM-CSF 11 Ok ✅ 0.849 0.752–0.96 0.008 ** ⬇
GRO-alpha (CXCL1) 16 Ok ✅ 0.987 0.980–0.99 0.000 *** ⬇
IL-1α 1 Sparse Data ❌ 0.258 0.011–6.02 0.399 ⬇
IL-1β 7 Ok ✅ 0.843 0.721–0.99 0.034 * ⬇
IL-1RA 15 Ok ✅ 1.000 1.000–1.00 0.007 ** ⬇
IL-2 2 Sparse Data ❌ 0.374 0.283–0.49 0.000 *** ⬇
IL-5 7 Ok ✅ 0.893 0.678–1.18 0.420 ⬇
IL-6 15 Ok ✅ 0.998 0.997–1.00 0.000 *** ⬇
IL-7 16 Ok ✅ 1.460 0.378–5.64 0.583 ⬆
IL-8 (CXCL8) 16 Ok ✅ 0.999 0.998–1.00 0.140 ⬇
IL-9 1 Sparse Data ❌ 1.331 0.731–2.42 0.349 ⬆
IL-10 10 Ok ✅ 0.823 0.685–0.99 0.038 * ⬇
IL-15 7 Ok ✅ 0.249 0.119–0.52 0.000 *** ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 0.745 0.250–2.22 0.596 ⬇
IL-18 16 Ok ✅ 1.008 0.991–1.03 0.359 ⬆
IL-31 1 Sparse Data ❌ 0.911 0.689–1.20 0.509 ⬇
IP-10 (CXCL10) 16 Ok ✅ 1.000 0.999–1.00 0.746 ⬇
MCP-1 (CCL2) 16 Ok ✅ 1.000 0.999–1.00 0.987 ⬇
MIP-1α (CCL3) 16 Ok ✅ 0.903 0.827–0.99 0.024 * ⬇
MIP-1β (CCL4) 16 Ok ✅ 0.987 0.969–1.01 0.157 ⬇
RANTES (CCL5) 16 Ok ✅ 1.001 0.935–1.07 0.987 ⬆
SDF-1α 16 Ok ✅ 1.000 0.999–1.00 0.296 ⬇
TNF-α 3 Sparse Data ❌ 0.380 0.220–0.66 0.001 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok ✅ 1.079 0.9085–1.28 0.387 ⬆
GM-CSF 11 Ok ✅ 0.853 0.7522–0.97 0.013 * ⬇
GRO-alpha (CXCL1) 16 Ok ✅ 0.989 0.9803–1.00 0.007 ** ⬇
IL-1α 1 Sparse Data ❌ 0.295 0.0061–14.32 0.538 ⬇
IL-1β 7 Ok ✅ 0.824 0.6595–1.03 0.089 . ⬇
IL-1RA 15 Ok ✅ 1.000 0.9995–1.00 0.000 *** ⬇
IL-2 2 Sparse Data ❌ 0.385 0.3005–0.49 0.000 *** ⬇
IL-5 7 Ok ✅ 0.913 0.7685–1.08 0.300 ⬇
IL-6 15 Ok ✅ 0.998 0.9973–1.00 0.000 *** ⬇
IL-7 16 Ok ✅ 1.273 0.2751–5.89 0.757 ⬆
IL-8 (CXCL8) 16 Ok ✅ 0.999 0.9970–1.00 0.241 ⬇
IL-9 1 Sparse Data ❌ 1.235 0.7163–2.13 0.448 ⬆
IL-10 10 Ok ✅ 0.794 0.6189–1.02 0.069 . ⬇
IL-15 7 Ok ✅ 0.286 0.1314–0.62 0.002 ** ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 1.011 0.2677–3.82 0.987 ⬆
IL-18 16 Ok ✅ 1.010 0.9911–1.03 0.313 ⬆
IL-31 1 Sparse Data ❌ 0.955 0.7104–1.28 0.762 ⬇
IP-10 (CXCL10) 16 Ok ✅ 1.000 0.9988–1.00 0.576 ⬆
MCP-1 (CCL2) 16 Ok ✅ 1.000 0.9987–1.00 0.763 ⬇
MIP-1α (CCL3) 16 Ok ✅ 0.883 0.7876–0.99 0.032 * ⬇
MIP-1β (CCL4) 16 Ok ✅ 0.988 0.9626–1.01 0.343 ⬇
RANTES (CCL5) 16 Ok ✅ 1.024 0.9410–1.11 0.581 ⬆
SDF-1α 16 Ok ✅ 1.000 0.9976–1.00 0.996 ⬇
TNF-α 3 Sparse Data ❌ 0.358 0.2383–0.54 0.000 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok ✅ 1.024 0.84274–1.25 0.809 ⬆
GM-CSF 11 Ok ✅ 0.912 0.74469–1.12 0.373 ⬇
GRO-alpha (CXCL1) 16 Ok ✅ 0.986 0.97556–1.00 0.008 ** ⬇
IL-1α 1 Sparse Data ❌ 0.001 0.00002–0.05 0.001 *** ⬇
IL-1β 7 Ok ✅ 0.881 0.69896–1.11 0.282 ⬇
IL-1RA 15 Ok ✅ 0.999 0.99921–1.00 0.000 *** ⬇
IL-2 2 Sparse Data ❌ 0.411 0.24355–0.69 0.001 *** ⬇
IL-5 7 Ok ✅ 1.205 0.82781–1.75 0.331 ⬆
IL-6 15 Ok ✅ 0.998 0.99624–1.00 0.032 * ⬇
IL-7 16 Ok ✅ 0.369 0.08496–1.61 0.184 ⬇
IL-8 (CXCL8) 16 Ok ✅ 0.999 0.99734–1.00 0.512 ⬇
IL-9 1 Sparse Data ❌ 0.562 0.26925–1.17 0.125 ⬇
IL-10 10 Ok ✅ 0.999 0.72760–1.37 0.996 ⬇
IL-15 7 Ok ✅ 0.279 0.07528–1.03 0.056 . ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 1.601 0.24895–10.29 0.620 ⬆
IL-18 16 Ok ✅ 1.013 0.98636–1.04 0.334 ⬆
IL-31 1 Sparse Data ❌ 0.882 0.64825–1.20 0.425 ⬇
IP-10 (CXCL10) 16 Ok ✅ 1 0.99786–1.00 0.840 ⬆
MCP-1 (CCL2) 16 NA Estimates ❌ 0.999 NA–NA NaN ⬇
MIP-1α (CCL3) 16 Wide CI ❌ >1e6 <1e-6–+Inf 0.963 ⬆
MIP-1β (CCL4) 16 Ok ✅ 0.971 0.93720–1.01 0.108 ⬇
RANTES (CCL5) 16 NA Estimates ❌ 0.933 NA–NA NaN ⬇
SDF-1α 16 Ok ✅ 1 0.99906–1.00 0.605 ⬆
TNF-α 3 Sparse Data ❌ 0.445 0.19842–1.00 0.049 * ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

Model Results: Longitudinal / state-level data

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok ✅ 1.050 0.925–1.19 0.447 ⬆
GM-CSF 8 Ok ✅ 0.943 0.870–1.02 0.154 ⬇
GRO-alpha (CXCL1) 13 Ok ✅ 0.996 0.992–1.00 0.076 . ⬇
IL-1α 1 Sparse Data ❌ 0.074 0.013–0.41 0.003 ** ⬇
IL-1β 5 Ok ✅ 0.974 0.848–1.12 0.711 ⬇
IL-1RA 12 Ok ✅ 1.000 1.000–1.00 0.000 *** ⬇
IL-2 2 Sparse Data ❌ 0.689 0.530–0.89 0.005 ** ⬇
IL-5 5 Ok ✅ 0.990 0.899–1.09 0.831 ⬇
IL-6 12 Ok ✅ 0.999 0.999–1.00 0.032 * ⬇
IL-7 13 Ok ✅ 1.515 0.581–3.95 0.395 ⬆
IL-8 (CXCL8) 13 Ok ✅ 0.998 0.997–1.00 0.000 *** ⬇
IL-9 1 Sparse Data ❌ 0.785 0.551–1.12 0.178 ⬇
IL-10 8 Ok ✅ 0.657 0.489–0.88 0.005 ** ⬇
IL-15 5 Ok ✅ 0.702 0.424–1.16 0.169 ⬇
IL-17A/CTLA-8 1 Sparse Data ❌ 0.660 0.235–1.85 0.430 ⬇
IL-18 13 Ok ✅ 1.011 1.001–1.02 0.034 * ⬆
IL-31 1 Sparse Data ❌ 1.042 0.882–1.23 0.626 ⬆
IP-10 (CXCL10) 13 Ok ✅ 1.000 0.999–1.00 0.963 ⬆
MCP-1 (CCL2) 13 Ok ✅ 0.999 0.999–1.00 0.000 *** ⬇
MIP-1α (CCL3) 13 Ok ✅ 0.957 0.887–1.03 0.255 ⬇
MIP-1β (CCL4) 13 Ok ✅ 0.984 0.969–1.00 0.034 * ⬇
RANTES (CCL5) 13 Ok ✅ 1.006 0.962–1.05 0.794 ⬆
SDF-1α 13 Ok ✅ 1.000 0.999–1.00 0.930 ⬆
TNF-α 2 Sparse Data ❌ 0.610 0.457–0.81 0.001 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Negative Binomial Models (per transition)
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok ✅ 1.030 0.931–1.14 0.565 ⬆
GM-CSF 10 Ok ✅ 0.912 0.849–0.98 0.013 * ⬇
GRO-alpha (CXCL1) 15 Ok ✅ 0.995 0.991–1.00 0.020 * ⬇
IL-1α 1 Sparse Data ❌ 0.744 0.069–7.97 0.807 ⬇
IL-1β 7 Ok ✅ 0.890 0.776–1.02 0.095 . ⬇
IL-1RA 14 Ok ✅ 1.000 1.000–1.00 0.197 ⬇
IL-2 2 Sparse Data ❌ 0.607 0.490–0.75 0.000 *** ⬇
IL-5 6 Ok ✅ 0.953 0.864–1.05 0.342 ⬇
IL-6 14 Ok ✅ 0.999 0.999–1.00 0.025 * ⬇
IL-7 15 Ok ✅ 1.297 0.464–3.62 0.620 ⬆
IL-8 (CXCL8) 15 Ok ✅ 0.999 0.998–1.00 0.387 ⬇
IL-9 1 Sparse Data ❌ 1.121 0.794–1.58 0.516 ⬆
IL-10 10 Ok ✅ 0.880 0.738–1.05 0.159 ⬇
IL-15 6 Ok ✅ 0.528 0.346–0.80 0.003 ** ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 1.072 0.469–2.45 0.870 ⬆
IL-18 15 Ok ✅ 1.007 0.996–1.02 0.223 ⬆
IL-31 1 Sparse Data ❌ 0.945 0.794–1.13 0.527 ⬇
IP-10 (CXCL10) 15 Ok ✅ 1.000 0.999–1.00 0.862 ⬆
MCP-1 (CCL2) 15 Ok ✅ 1.000 0.999–1.00 0.958 ⬇
MIP-1α (CCL3) 15 Ok ✅ 0.917 0.854–0.99 0.018 * ⬇
MIP-1β (CCL4) 15 Ok ✅ 0.991 0.976–1.01 0.245 ⬇
RANTES (CCL5) 15 Ok ✅ 1.004 0.957–1.05 0.875 ⬆
SDF-1α 15 Ok ✅ 1.000 0.999–1.00 0.551 ⬇
TNF-α 3 Sparse Data ❌ 0.574 0.418–0.79 0.001 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok ✅ 1.030 0.936–1.13 0.542 ⬆
GM-CSF 10 Ok ✅ 0.925 0.863–0.99 0.029 * ⬇
GRO-alpha (CXCL1) 15 Ok ✅ 0.995 0.991–1.00 0.026 * ⬇
IL-1α 1 Sparse Data ❌ 0.465 0.054–3.98 0.485 ⬇
IL-1β 7 Ok ✅ 0.903 0.795–1.03 0.115 ⬇
IL-1RA 14 Ok ✅ 1.000 1.000–1.00 0.013 * ⬇
IL-2 2 Sparse Data ❌ 0.624 0.510–0.76 0.000 *** ⬇
IL-5 6 Ok ✅ 0.965 0.878–1.06 0.455 ⬇
IL-6 14 Ok ✅ 0.999 0.999–1.00 0.023 * ⬇
IL-7 15 Ok ✅ 1.306 0.503–3.39 0.584 ⬆
IL-8 (CXCL8) 15 Ok ✅ 0.999 0.998–1.00 0.206 ⬇
IL-9 1 Sparse Data ❌ 1.061 0.773–1.46 0.712 ⬆
IL-10 10 Ok ✅ 0.891 0.760–1.04 0.153 ⬇
IL-15 6 Ok ✅ 0.570 0.374–0.87 0.009 ** ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 0.967 0.450–2.08 0.932 ⬇
IL-18 15 Ok ✅ 1.007 0.997–1.02 0.167 ⬆
IL-31 1 Sparse Data ❌ 0.958 0.815–1.13 0.599 ⬇
IP-10 (CXCL10) 15 Ok ✅ 1.000 0.999–1.00 0.866 ⬆
MCP-1 (CCL2) 15 Ok ✅ 1.000 0.999–1.00 0.796 ⬇
MIP-1α (CCL3) 15 Ok ✅ 0.923 0.865–0.99 0.017 * ⬇
MIP-1β (CCL4) 15 Ok ✅ 0.990 0.976–1.00 0.149 ⬇
RANTES (CCL5) 15 Ok ✅ 1.006 0.962–1.05 0.779 ⬆
SDF-1α 15 Ok ✅ 1.000 1.000–1.00 0.671 ⬇
TNF-α 3 Sparse Data ❌ 0.590 0.446–0.78 0.000 *** ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok ✅ 1.291 0.88–1.88 0.185 ⬆
GM-CSF 10 Ok ✅ 0.702 0.35–1.42 0.325 ⬇
GRO-alpha (CXCL1) 15 Ok ✅ 0.538 0.28–1.03 0.060 . ⬇
IL-1α 1 Sparse Data ❌ 0.661 0.51–0.86 0.002 ** ⬇
IL-1β 6 Ok ✅ 0.950 0.51–1.77 0.871 ⬇
IL-1RA 14 Ok ✅ 0.469 0.33–0.67 0.000 *** ⬇
IL-2 2 Sparse Data ❌ 0.583 0.36–0.93 0.025 * ⬇
IL-5 7 Ok ✅ 0.837 0.40–1.77 0.641 ⬇
IL-6 14 Ok ✅ 0.513 0.28–0.93 0.029 * ⬇
IL-7 15 Ok ✅ 1.186 0.73–1.92 0.491 ⬆
IL-8 (CXCL8) 15 Ok ✅ 0.770 0.52–1.13 0.185 ⬇
IL-9 1 Sparse Data ❌ 0.640 0.47–0.86 0.004 ** ⬇
IL-10 9 Ok ✅ 1.029 0.65–1.62 0.901 ⬆
IL-15 7 Ok ✅ 0.676 0.43–1.06 0.085 . ⬇
IL-17A/CTLA-8 2 Sparse Data ❌ 1.236 0.85–1.79 0.264 ⬆
IL-18 15 Ok ✅ 1.636 0.92–2.90 0.092 . ⬆
IL-31 1 Sparse Data ❌ 1.155 0.63–2.11 0.639 ⬆
IP-10 (CXCL10) 15 Ok ✅ 1.166 0.83–1.64 0.377 ⬆
MCP-1 (CCL2) 15 Ok ✅ 0.494 0.31–0.80 0.004 ** ⬇
MIP-1α (CCL3) 15 Ok ✅ 0.811 0.44–1.50 0.502 ⬇
MIP-1β (CCL4) 15 Ok ✅ 0.849 0.57–1.25 0.412 ⬇
RANTES (CCL5) 15 Ok ✅ 0.988 0.65–1.49 0.953 ⬇
SDF-1α 15 Ok ✅ 0.979 0.60–1.60 0.933 ⬇
TNF-α 3 Sparse Data ❌ 0.555 0.32–0.97 0.038 * ⬇
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

AIM 1: Reservoirs Dynamic and Reservoirs Characteristics

Model Results: Longitudinal / state-level data

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify reservoir characteristics associated with dynamics
Associations between Reservoirs Measures and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok ✅ 0.875 7.0e-01–1.1e+00 0.224 ⬇
HIV DNA (destination) Ok ✅ 1.007 8.5e-01–1.2e+00 0.932 ⬆
Viral Diversity (source) Ok ✅ >1e6 1.7e+05–+Inf 0.004 ** ⬆
Viral Diversity (destination) Ok ✅ >1e6 9.4e-03–+Inf 0.139 ⬆
Viral Divergence Wide CI ❌ 5.247 <1e-6–+Inf 0.888 ⬆
Associations between Reservoirs Measures and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok ✅ 1.158 1.0e+00–1.3e+00 0.043 * ⬆
HIV DNA (destination) Ok ✅ 0.959 8.4e-01–1.1e+00 0.525 ⬇
Viral Diversity (source) Ok ✅ >1e6 5.6e+00–+Inf 0.029 * ⬆
Viral Diversity (destination) Wide CI ❌ 0.586 <1e-6–+Inf 0.951 ⬇
Viral Divergence Wide CI ❌ 0.005 <1e-6–+Inf 0.605 ⬇
Associations between Reservoirs Measures and counts of migration events across the BBB
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok ✅ 1.163 1.1e+00–1.3e+00 0.003 ** ⬆
HIV DNA (destination) Ok ✅ 0.917 8.4e-01–1.0e+00 0.056 . ⬇
Viral Diversity (source) Ok ✅ >1e6 1.0e+05–+Inf 0.000 *** ⬆
Viral Diversity (destination) Ok ✅ 0.193 <1e-6–1.1e+05 0.808 ⬇
Viral Divergence Ok ✅ 0.031 <1e-6–9.3e+05 0.693 ⬇
Associations between Reservoirs Measures and counts of migration events within CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok ✅ 0.730 6.4e-01–8.4e-01 0.000 *** ⬇
HIV DNA (destination) Ok ✅ 1.103 9.8e-01–1.2e+00 0.098 . ⬆
Viral Diversity (source) Ok ✅ 2972.111 3.5e-05–+Inf 0.391 ⬆
Viral Diversity (destination) Ok ✅ 25988.049 5.1e-03–+Inf 0.197 ⬆
Viral Divergence Wide CI ❌ 57.962 <1e-6–+Inf 0.670 ⬆

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